Various electromagnetic techniques exist to perform surveys of subterranean structures underneath a surface for identifying structures of interest. Examples of structures of interest in the subterranean structure include subsurface resistive bodies, such as oil-bearing reservoirs, gas injection zones, and fresh-water aquifers. One survey technique is the controlled source electromagnetic (CSEM) survey technique, in which an electromagnetic (EM) transmitter (typically towed by a sea vessel in a marine environment) is used to generate electromagnetic signals.
Surveying units (or EM receivers) containing electric and magnetic field sensors are deployed on the sea floor within an area of interest to make measurements (of EM wavefields) from which a geological survey of the subterranean structure underneath the surface can be derived. Through the use of the CSEM technique, a high-resolution mapping of changes in resistivity associated with the presence of oil and hydrocarbon is possible. Measurements taken by the EM receivers are interpreted in such a way that a prediction of the presence and location of oil and hydrocarbon in the sedimentary layers of the subterranean structure can be made.
In a CSEM survey, the source signal produced by the EM transmitter (source) is typically recorded along with the survey data collected by the EM receivers. The source signal is usually recorded as a digital time series. Note that the source signal is a periodic signal that has a base period (the waveform of the source signal repeats every base period). For quality control, a characteristic (e.g., amplitude and/or phase) of the source signal is checked to ensure that the characteristic is consistent from base period to base period. Often, this checking of the characteristic of the source signal is accomplished by first transforming the time series representing the source signal into the frequency domain. The analysis of variations in the amplitude and/or phase of the source signal is then checked in the frequency domain. However, such analysis involves relatively heavy mathematical computations, since there typically is a relatively large volume of data, and transforming a large time series into the frequency domain can be computationally relatively expensive.